# Copyright 2024 Bytedance Ltd. and/or its affiliates
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
The main entry point to run the PPO algorithm
"""

import logging
import os
import time

import torch
import torch.distributed
from codetiming import Timer
from megatron.core import parallel_state as mpu
from omegaconf import DictConfig

from verl import DataProto
from verl.single_controller.base.decorator import Dispatch, register
from verl.single_controller.base.megatron.worker import MegatronWorker
from verl.utils import hf_tokenizer
from verl.utils.checkpoint.megatron_checkpoint_manager import MegatronCheckpointManager
from verl.utils.debug import log_gpu_memory_usage
from verl.utils.flops_counter import FlopsCounter
from verl.utils.fs import copy_to_local
from verl.utils.megatron_utils import (
    load_megatron_param_and_grad,
    offload_megatron_param_and_grad,
)
from verl.utils.model import load_mcore_dist_weights, load_megatron_gptmodel_weights
from verl.workers.actor.megatron_actor import MegatronPPOActor
from verl.workers.critic.megatron_critic import MegatronPPOCritic
from verl.workers.reward_model.megatron.reward_model import MegatronRewardModel

logger = logging.getLogger(__file__)
logger.setLevel(os.getenv("VERL_LOGGING_LEVEL", "WARN"))


def set_random_seed(seed):
    import random

    import numpy as np
    import torch

    torch.manual_seed(seed)
    np.random.seed(seed)
    random.seed(seed)
    if torch.cuda.device_count() > 0:
        from megatron.core import tensor_parallel

        tensor_parallel.model_parallel_cuda_manual_seed(seed)
    # FIXME: torch cumsum not support deterministic (used in vllm sampler),
    # https://github.com/pytorch/pytorch/issues/89492
    # torch.use_deterministic_algorithms(True, warn_only=True)
    # os.environ['CUBLAS_WORKSPACE_CONFIG'] = ':4096:8'


class ActorRolloutRefWorker(MegatronWorker):
    """
    This worker can be instantiated as a standalone actor or a standalone rollout or a standalone reference policy
    or a hybrid engine based on the config.rollout
    """

    def __init__(self, config: DictConfig, role: str):
        super().__init__()
        self.config = config

        # NOTE(sgm): We utilize colocate WorkerGroup by default.
        # As a result, Workers for different model share the same process.
        # Therefore, we only require one distribute initialization.
        # To utilize different parallel startegy in different models:
        # 1, users should disable WorkerDict; 2.assign different ResourcePool to different models,
        # 3. and apply the following patch in ray==2.10, https://github.com/ray-project/ray/pull/44385
        if not torch.distributed.is_initialized():
            rank = int(os.environ["LOCAL_RANK"])
            torch.distributed.init_process_group(backend="nccl")
            torch.cuda.set_device(rank)

            if self.config.actor.megatron.sequence_parallel:
                os.environ["CUDA_DEVICE_MAX_CONNECTIONS"] = "1"
            mpu.initialize_model_parallel(
                tensor_model_parallel_size=self.config.actor.megatron.tensor_model_parallel_size,
                pipeline_model_parallel_size=self.config.actor.megatron.pipeline_model_parallel_size,
                virtual_pipeline_model_parallel_size=self.config.actor.megatron.virtual_pipeline_model_parallel_size,
                pipeline_model_parallel_split_rank=None,
                use_sharp=False,
                context_parallel_size=self.config.actor.megatron.context_parallel_size,
                expert_model_parallel_size=1,
                nccl_communicator_config_path=None,
            )

        set_random_seed(seed=self.config.actor.megatron.seed)

        self.role = role
        assert self.role in ["actor", "rollout", "ref", "actor_rollout", "actor_rollout_ref"]

        self._is_actor = self.role in ["actor", "actor_rollout", "actor_rollout_ref"]
        self._is_rollout = self.role in ["rollout", "actor_rollout", "actor_rollout_ref"]
        self._is_ref = self.role in ["ref", "actor_rollout_ref"]

        # TODO(sgm): Currently, we only support reference model param offload
        # will support other offload later
        self._is_offload_param = False
        self._is_offload_grad = False
        self._is_offload_optimizer = False

        # normalize config
        if self._is_actor and self._is_rollout:
            self.config.actor.ppo_mini_batch_size *= self.config.rollout.n
            self.config.actor.ppo_mini_batch_size //= mpu.get_data_parallel_world_size()
            if self.config.actor.get("ppo_micro_batch_size", None):
                self.config.actor.ppo_micro_batch_size //= mpu.get_data_parallel_world_size()
                self.config.rollout.log_prob_micro_batch_size //= mpu.get_data_parallel_world_size()
                self.config.actor.ppo_micro_batch_size_per_gpu = self.config.actor.ppo_micro_batch_size
                self.config.rollout.log_prob_micro_batch_size_per_gpu = self.config.rollout.log_prob_micro_batch_size

            self._is_offload_param = self.config.actor.get("param_offload", False)
            self._is_offload_grad = self.config.actor.get("grad_offload", False)
            self._is_offload_optimizer = self.config.actor.get("optimizer_offload", False)
        elif self._is_ref:
            if self.config.ref.get("ppo_micro_batch_size", None):
                self.config.ref.log_prob_micro_batch_size //= mpu.get_data_parallel_world_size()
                self.config.ref.ppo_micro_batch_size_per_gpu = self.config.ref.ppo_micro_batch_size
            self._is_offload_param = self.config.ref.get("param_offload", False)

    def _build_model_optimizer(self, model_path, optim_config, override_model_config):
        from megatron.core.models.gpt.gpt_model import ModelType

        from verl.utils.megatron.optimizer import get_megatron_optimizer
        from verl.utils.megatron_utils import get_model, init_megatron_optim_config
        from verl.utils.model import get_generation_config, print_model_size

        self._init_hf_config_and_tf_config(model_path, self.dtype, override_model_config)
        self.generation_config = get_generation_config(self.local_path)

        def megatron_actor_model_provider(pre_process, post_process):
            from verl.models.mcore import init_mcore_model

            parallel_model = init_mcore_model(
                self.tf_config,
                self.hf_config,
                pre_process,
                post_process,
                share_embeddings_and_output_weights=self.share_embeddings_and_output_weights,
                value=False,
            )
            parallel_model.cuda()
            return parallel_model

        # Step 3: initialize the megatron model
        if self._is_actor and self._is_rollout:
            actor_module = get_model(
                megatron_actor_model_provider,
                wrap_with_ddp=True,
                use_distributed_optimizer=self.config.actor.megatron.use_distributed_optimizer,
            )
            print(f"actor_module: {len(actor_module)}")
            if self.config.actor.load_weight:
                if self.config.actor.megatron.use_dist_checkpointing:
                    load_mcore_dist_weights(
                        actor_module, self.config.actor.megatron.dist_checkpointing_path, is_value_model=False
                    )
                else:
                    load_megatron_gptmodel_weights(
                        self.config, self.hf_config, actor_module, params_dtype=self.dtype, is_value_model=False
                    )

            if self.rank == 0:
                print_model_size(actor_module[0])
            log_gpu_memory_usage("After MegatronPPOActor init", logger=logger)
        elif self._is_ref:
            print(f"self.config.ref.load_weight: {self.config.ref.load_weight}")
            ref_module = get_model(
                model_provider_func=megatron_actor_model_provider,
                model_type=ModelType.encoder_or_decoder,
                wrap_with_ddp=False,
                use_distributed_optimizer=self.config.ref.megatron.use_distributed_optimizer,
            )
            # ref_module = nn.ModuleList(ref_module)

            if self.config.ref.load_weight:  # should align with the actor:
                assert self.config.actor.load_weight == self.config.ref.load_weight
                print("load ref weight start")
                if self.config.ref.megatron.use_dist_checkpointing:
                    load_mcore_dist_weights(
                        ref_module, self.config.ref.megatron.dist_checkpointing_path, is_value_model=False
                    )
                else:
                    load_megatron_gptmodel_weights(
                        self.config, self.hf_config, ref_module, params_dtype=self.dtype, is_value_model=False
                    )
            log_gpu_memory_usage("After ref module init", logger=logger)
            return ref_module, self.hf_config

        # TODO: add more optimizer args into config
        if self._is_actor:
            optim_config = init_megatron_optim_config(optim_config)
            actor_optimizer = get_megatron_optimizer(model=actor_module, config=optim_config)
        else:
            optim_config = None
            actor_optimizer = None

        log_gpu_memory_usage("After actor optimizer init", logger=logger)

        return actor_module, actor_optimizer, self.hf_config, optim_config

    def _build_rollout(self, trust_remote_code=False):
        if self.config.rollout.name == "vllm":
            from torch.distributed.device_mesh import init_device_mesh

            from verl.workers.rollout.vllm_rollout import vllm_mode, vLLMRollout
            from verl.workers.sharding_manager.megatron_vllm import MegatronVLLMShardingManager

            # NOTE(sgm): If the QKV and gate_up projection layer are concate together in actor,
            # we will reorganize their weight format when resharding from actor to rollout.
            layer_name_mapping = {
                "qkv_layer_name": "self_attention.linear_qkv.",
                "gate_proj_layer_name": "linear_fc1.weight",
            }

            infer_tp = self.config.rollout.tensor_model_parallel_size
            dp = self.world_size // infer_tp
            assert self.world_size % infer_tp == 0, (
                f"rollout world_size: {self.world_size} is not divisible by infer_tp: {infer_tp}"
            )
            rollout_device_mesh = init_device_mesh("cuda", mesh_shape=(dp, infer_tp), mesh_dim_names=["dp", "infer_tp"])
            log_gpu_memory_usage("Before building vllm rollout", logger=None)

            local_path = copy_to_local(self.config.model.path)
            if vllm_mode == "customized":
                rollout = vLLMRollout(
                    actor_module=self.actor_module,
                    config=self.config.rollout,
                    tokenizer=self.tokenizer,
                    model_hf_config=self.actor_model_config,
                )
            elif vllm_mode == "spmd":
                rollout = vLLMRollout(
                    model_path=local_path,
                    config=self.config.rollout,
                    tokenizer=self.tokenizer,
                    model_hf_config=self.actor_model_config,
                    device_mesh=rollout_device_mesh,
                    trust_remote_code=trust_remote_code,
                )
            log_gpu_memory_usage("After building vllm rollout", logger=logger)

            # perform weight resharding between actor and rollout
            from verl.models.mcore import get_mcore_weight_converter

            weight_converter = get_mcore_weight_converter(self.actor_model_config, self.dtype)
            sharding_manager = MegatronVLLMShardingManager(
                inference_engine=rollout.inference_engine,
                model_config=self.actor_model_config,
                layer_name_mapping=layer_name_mapping,
                actor_module=self.actor.actor_module,
                weight_converter=weight_converter,
            )
            log_gpu_memory_usage("After building sharding manager", logger=logger)
        else:
            raise NotImplementedError("Only vllmRollout is supported with Megatron now")

        return rollout, sharding_manager

    @register(dispatch_mode=Dispatch.ONE_TO_ALL)
    def init_model(self):
        if self.config.model.get("external_lib", None) is not None:
            # This is used to import external_lib into the huggingface systems
            import importlib

            importlib.import_module(self.config.model.external_lib)

        from omegaconf import OmegaConf

        from verl.utils.torch_dtypes import PrecisionType

        override_model_config = OmegaConf.to_container(self.config.model.get("override_config", OmegaConf.create()))
        self.param_dtype = torch.bfloat16

        self.dtype = PrecisionType.to_dtype(self.param_dtype)
        if self._is_actor or self._is_rollout:
            # we need the model for actor and rollout
            optim_config = self.config.actor.optim if self._is_actor else None
            self.actor_module, self.actor_optimizer, self.actor_model_config, self.actor_optim_config = (
                self._build_model_optimizer(
                    model_path=self.config.model.path,
                    optim_config=optim_config,
                    override_model_config=override_model_config,
                )
            )

        if self._is_actor:
            self.actor = MegatronPPOActor(
                config=self.config.actor,
                model_config=self.actor_model_config,
                hf_config=self.hf_config,
                tf_config=self.tf_config,
                actor_module=self.actor_module,
                actor_optimizer=self.actor_optimizer,
            )

        if self._is_rollout:
            self.rollout, self.sharding_manager = self._build_rollout(
                trust_remote_code=self.config.model.get("trust_remote_code", False)
            )

        if self._is_ref:
            self.ref_module, self.ref_model_config = self._build_model_optimizer(
                model_path=self.config.model.path,
                optim_config=None,
                override_model_config=override_model_config,
            )
            self.ref_policy = MegatronPPOActor(
                config=self.config.ref,
                model_config=self.ref_model_config,
                hf_config=self.hf_config,
                tf_config=self.tf_config,
                actor_module=self.ref_module,
                actor_optimizer=None,
            )

        if self._is_actor:
            self.flops_counter = FlopsCounter(self.actor_model_config)
            self.checkpoint_mananager = MegatronCheckpointManager(
                config=self.config,
                model_config=self.actor_model_config,
                role="actor",
                model=self.actor_module,
                arch=self.architectures[0],
                hf_config=self.hf_config,
                param_dtype=self.param_dtype,
                share_embeddings_and_output_weights=self.share_embeddings_and_output_weights,
                tokenizer=self.tokenizer,
                optimizer=self.actor_optimizer,
                use_distributed_optimizer=self.config.actor.megatron.use_distributed_optimizer,
                checkpoint_contents=self.config.actor.checkpoint.contents,
            )

        torch.cuda.empty_cache()

    @register(dispatch_mode=Dispatch.MEGATRON_COMPUTE_PROTO)
    def update_actor(self, data: DataProto):
        assert self._is_actor

        data.batch = data.batch.cuda()

        micro_batch_size = self.config.actor.ppo_micro_batch_size_per_gpu
        data.meta_info["micro_batch_size"] = micro_batch_size
        dataloader = self.actor.make_minibatch_iterator(data=data)
        with Timer(name="update_policy", logger=None) as timer:
            metrics = self.actor.update_policy(dataloader=dataloader)
        delta_time = timer.last
        global_num_tokens = data.meta_info["global_token_num"]
        estimated_flops, promised_flops = self.flops_counter.estimate_flops(global_num_tokens, delta_time)
        metrics["perf/mfu/actor"] = estimated_flops * self.config.actor.ppo_epochs / promised_flops / self.world_size

        # TODO: here, we should return all metrics
        output = DataProto(meta_info={"metrics": metrics})
        output = output.to("cpu")
        torch.cuda.empty_cache()
        return output

    @register(dispatch_mode=Dispatch.MEGATRON_PP_AS_DP_PROTO)
    def generate_sequences(self, prompts: DataProto):
        assert self._is_rollout

        prompts.batch = prompts.batch.cuda()
        meta_info = {
            "eos_token_id": self.generation_config.eos_token_id
            if self.generation_config is not None
            else self.tokenizer.eos_token_id,
            "pad_token_id": self.generation_config.pad_token_id
            if self.generation_config is not None
            else self.tokenizer.pad_token_id,
        }
        prompts.meta_info.update(meta_info)
        with self.sharding_manager:
            prompts = self.sharding_manager.preprocess_data(prompts)
            output = self.rollout.generate_sequences(prompts=prompts)
            output = self.sharding_manager.postprocess_data(output)

        output = output.to("cpu")
        # clear kv cache
        torch.cuda.empty_cache()
        return output

    @register(dispatch_mode=Dispatch.MEGATRON_COMPUTE_PROTO)
    def compute_ref_log_prob(self, data: DataProto):
        data = data.to("cuda")

        assert self._is_ref
        if self._is_offload_param:
            load_megatron_param_and_grad(self.ref_module, torch.cuda.current_device(), self._is_offload_grad)

        micro_batch_size = self.config.ref.log_prob_micro_batch_size_per_gpu
        data.meta_info["micro_batch_size"] = micro_batch_size
        data.meta_info["temperature"] = self.config.rollout.temperature
        output, _ = self.ref_policy.compute_log_prob(data=data, calculate_entropy=False)
        output = DataProto.from_dict(tensors={"ref_log_prob": output})
        output = output.to("cpu")
        if self._is_offload_param:
            offload_megatron_param_and_grad(self.ref_module, self._is_offload_grad)
        torch.cuda.empty_cache()
        return output

    @register(dispatch_mode=Dispatch.MEGATRON_COMPUTE_PROTO)
    def compute_log_prob(self, data: DataProto):
        assert self._is_actor
        data = data.to("cuda")
        output = data
        # we should always recompute old_log_probs when it is HybridEngine
        output.meta_info["micro_batch_size"] = self.config.rollout.log_prob_micro_batch_size_per_gpu
        output.meta_info["temperature"] = self.config.rollout.temperature
        old_log_probs, entropys = self.actor.compute_log_prob(data=output, calculate_entropy=True)
        output.batch["old_log_probs"] = old_log_probs
        output.batch["entropys"] = entropys
        output = output.to("cpu")
        # clear kv cache
        torch.cuda.empty_cache()
        return output

    @register(dispatch_mode=Dispatch.ONE_TO_ALL)
    def load_checkpoint(self, checkpoint_path, hdfs_path=None, del_local_after_load=True):
        self.checkpoint_mananager.load_checkpoint(
            local_path=checkpoint_path, hdfs_path=hdfs_path, del_local_after_load=del_local_after_load
        )

    @register(dispatch_mode=Dispatch.ONE_TO_ALL)
    def load_pretrained_model(self, checkpoint_path, del_local_after_load=True):
        pass

    @register(dispatch_mode=Dispatch.ONE_TO_ALL)
    def save_checkpoint(self, checkpoint_path, hdfs_path=None, global_step=0, max_ckpt_to_keep=None):
        self.checkpoint_mananager.save_checkpoint(
            local_path=checkpoint_path, hdfs_path=hdfs_path, global_step=global_step, max_ckpt_to_keep=max_ckpt_to_keep
        )


class CriticWorker(MegatronWorker):
    def __init__(self, config):
        super().__init__()
        self.config = config

        # NOTE(sgm): We utilize colocate WorkerGroup by default.
        # As a result, Workers for different model share the same process.
        # Therefore, we only require one distribute initialization.
        # To utilize different parallel startegy in different models:
        # 1, users should disable WorkerDict; 2.assign different ResourcePool to different models,
        # 3. and apply the following patch in ray==2.10, https://github.com/ray-project/ray/pull/44385
        if not torch.distributed.is_initialized():
            rank = int(os.environ["LOCAL_RANK"])
            torch.distributed.init_process_group(backend="nccl")
            torch.cuda.set_device(rank)

            if self.config.megatron.sequence_parallel:
                os.environ["CUDA_DEVICE_MAX_CONNECTIONS"] = "1"
            mpu.initialize_model_parallel(
                tensor_model_parallel_size=self.config.megatron.tensor_model_parallel_size,
                pipeline_model_parallel_size=self.config.megatron.pipeline_model_parallel_size,
                virtual_pipeline_model_parallel_size=self.config.megatron.virtual_pipeline_model_parallel_size,
                pipeline_model_parallel_split_rank=None,
                use_sharp=False,
                context_parallel_size=self.config.megatron.context_parallel_size,
                expert_model_parallel_size=1,
                nccl_communicator_config_path=None,
            )

        set_random_seed(seed=self.config.megatron.seed)

        # normalize config
        self.config.ppo_mini_batch_size *= self.config.rollout_n
        self.config.ppo_mini_batch_size //= mpu.get_data_parallel_world_size()
        if self.config.get("ppo_micro_batch_size", None):
            self.config.ppo_micro_batch_size //= mpu.get_data_parallel_world_size()
            self.config.ppo_micro_batch_size_per_gpu = self.config.ppo_micro_batch_size

        # TODO(sgm): support critic model offload

    def _build_critic_model_optimizer(self, model_path, optim_config, override_model_config):
        from megatron.core.models.gpt.gpt_model import ModelType

        from verl.utils.megatron.optimizer import get_megatron_optimizer
        from verl.utils.megatron_utils import get_model, init_megatron_optim_config
        from verl.utils.model import print_model_size

        self._init_hf_config_and_tf_config(model_path, self.dtype, override_model_config)

        def megatron_critic_model_provider(pre_process, post_process):
            from verl.models.mcore import init_mcore_model

            parallel_model = init_mcore_model(
                self.tf_config,
                self.hf_config,
                pre_process,
                post_process,
                share_embeddings_and_output_weights=False,
                value=True,
            )
            parallel_model.cuda()
            return parallel_model

        # Step 3: initialize the megatron model
        critic_module = get_model(
            model_provider_func=megatron_critic_model_provider,
            model_type=ModelType.encoder_or_decoder,
            wrap_with_ddp=True,
            use_distributed_optimizer=self.config.megatron.use_distributed_optimizer,
        )
        # note that here critic_module will be a list to be compatible with the construction of interleaved pp (vpp).
        # but here, we do not use pp (vpp) yet. For simplicity, we remove the list
        # critic_module = nn.ModuleList(critic_module)

        if self.config.load_weight:
            t0 = time.time()
            if self.config.megatron.use_dist_checkpointing:
                load_mcore_dist_weights(
                    critic_module, self.config.megatron.dist_checkpointing_path, is_value_model=True
                )
            else:
                load_megatron_gptmodel_weights(
                    self.config, self.hf_config, critic_module, params_dtype=self.dtype, is_value_model=True
                )
            t1 = time.time()
            if torch.distributed.get_rank() == 0:
                print(f"critic load_weight time: {t1 - t0}")
        if self.rank == 0:
            print_model_size(critic_module[0])

        # TODO: add more optimizer args into config
        optim_config = init_megatron_optim_config(optim_config)
        critic_optimizer = get_megatron_optimizer(model=critic_module, config=optim_config)
        torch.cuda.empty_cache()
        return critic_module, critic_optimizer, self.hf_config, optim_config

    @register(dispatch_mode=Dispatch.ONE_TO_ALL)
    def init_model(self):
        # create critic
        from omegaconf import OmegaConf

        from verl.utils.torch_dtypes import PrecisionType

        if self.config.model.get("external_lib", None) is not None:
            # This is used to import external_lib into the huggingface systems
            import importlib

            importlib.import_module(self.config.model.external_lib)
        override_model_config = OmegaConf.to_container(self.config.model.get("override_config", OmegaConf.create()))
        self.param_dtype = torch.bfloat16
        self.dtype = PrecisionType.to_dtype(self.param_dtype)
        self.critic_module, self.critic_optimizer, self.critic_model_config, critic_optimizer_config = (
            self._build_critic_model_optimizer(
                model_path=self.config.model.path,
                optim_config=self.config.optim,
                override_model_config=override_model_config,
            )
        )
        self.critic = MegatronPPOCritic(
            config=self.config,
            model_config=self.critic_model_config,
            hf_config=self.hf_config,
            tf_config=self.tf_config,
            critic_module=self.critic_module,
            critic_optimizer=self.critic_optimizer,
            critic_optimizer_config=critic_optimizer_config,
        )
        self.flops_counter = FlopsCounter(self.critic_model_config)
        self.checkpoint_mananager = MegatronCheckpointManager(
            config=self.config,
            model_config=self.critic_model_config,
            role="critic",
            model=self.critic_module,
            arch=self.architectures[0],
            hf_config=self.hf_config,
            param_dtype=self.param_dtype,
            share_embeddings_and_output_weights=False,
            tokenizer=self.tokenizer,
            optimizer=self.critic_optimizer,
            use_distributed_optimizer=self.config.megatron.use_distributed_optimizer,
            checkpoint_contents=self.config.checkpoint.contents,
        )

    @register(dispatch_mode=Dispatch.MEGATRON_COMPUTE_PROTO)
    def compute_values(self, data: DataProto):
        data = data.to("cuda")
        values = self.critic.compute_values(data=data)
        output = DataProto.from_dict(tensors={"values": values})
        output = output.to("cpu")
        return output

    @register(dispatch_mode=Dispatch.MEGATRON_COMPUTE_PROTO)
    def update_critic(self, data: DataProto):
        data = data.to("cuda")
        dataloader = self.critic.make_minibatch_iterator(data)
        with Timer(name="update_critic", logger=None) as timer:
            metrics = self.critic.update_critic(dataloader=dataloader)
        delta_time = timer.last
        global_num_tokens = data.meta_info["global_token_num"]
        estimated_flops, promised_flops = self.flops_counter.estimate_flops(global_num_tokens, delta_time)
        metrics["perf/mfu/critic"] = estimated_flops * self.config.ppo_epochs / promised_flops / self.world_size
        output = DataProto(batch=None, meta_info={"metrics": metrics})
        output = output.to("cpu")
        return output

    @register(dispatch_mode=Dispatch.ONE_TO_ALL)
    def load_checkpoint(self, checkpoint_path, hdfs_path=None, del_local_after_load=True):
        self.checkpoint_mananager.load_checkpoint(
            local_path=checkpoint_path, hdfs_path=hdfs_path, del_local_after_load=del_local_after_load
        )

    @register(dispatch_mode=Dispatch.ONE_TO_ALL)
    def save_checkpoint(self, checkpoint_path, hdfs_path=None, global_steps=0, max_ckpt_to_keep=None):
        self.checkpoint_mananager.save_checkpoint(
            local_path=checkpoint_path, hdfs_path=hdfs_path, global_step=global_steps, max_ckpt_to_keep=max_ckpt_to_keep
        )


class RewardModelWorker(MegatronWorker):
    """
    Note that we only implement the reward model that is subclass of AutoModelForSequenceClassification.
    """

    def __init__(self, config):
        super().__init__()
        self.config = config

        # NOTE(sgm): We utilize colocate WorkerGroup by default.
        # As a result, Workers for different model share the same process.
        # Therefore, we only require one distribute initialization.
        # To utilize different parallel startegy in different models:
        # 1, users should disable WorkerDict; 2.assign different ResourcePool to different models,
        # 3. and apply the following patch in ray==2.10, https://github.com/ray-project/ray/pull/44385
        if not torch.distributed.is_initialized():
            rank = int(os.environ["LOCAL_RANK"])
            torch.distributed.init_process_group(backend="nccl")
            torch.cuda.set_device(rank)

            if self.config.megatron.sequence_parallel:
                os.environ["CUDA_DEVICE_MAX_CONNECTIONS"] = "1"
            mpu.initialize_model_parallel(
                tensor_model_parallel_size=self.config.megatron.tensor_model_parallel_size,
                pipeline_model_parallel_size=self.config.megatron.pipeline_model_parallel_size,
                virtual_pipeline_model_parallel_size=self.config.megatron.virtual_pipeline_model_parallel_size,
                pipeline_model_parallel_split_rank=None,
                use_sharp=False,
                context_parallel_size=self.config.megatron.context_parallel_size,
                expert_model_parallel_size=1,
                nccl_communicator_config_path=None,
            )

        set_random_seed(seed=self.config.megatron.seed)

        # normalize config
        if self.config.micro_batch_size is not None:
            self.config.micro_batch_size //= mpu.get_data_parallel_world_size()
            self.config.micro_batch_size_per_gpu = self.config.micro_batch_size

    def _build_rm_model(self, model_path, override_model_config):
        from megatron.core.models.gpt.gpt_model import ModelType

        from verl.utils.megatron_utils import get_model

        self._init_hf_config_and_tf_config(model_path, self.dtype, override_model_config)

        def megatron_rm_model_provider(pre_process, post_process):
            from verl.models.mcore import init_mcore_model

            parallel_model = init_mcore_model(
                self.tf_config,
                self.hf_config,
                pre_process,
                post_process,
                share_embeddings_and_output_weights=False,
                value=True,
            )
            parallel_model.cuda()
            return parallel_model

        # Step 3: initialize the megatron model
        reward_model = get_model(
            model_provider_func=megatron_rm_model_provider,
            model_type=ModelType.encoder_or_decoder,
            wrap_with_ddp=False,
            use_distributed_optimizer=self.config.reward_model.use_distributed_optimizer,
        )
        # note that here critic_module will be a list to be compatible with the construction of interleaved pp (vpp).
        # but here, we do not use pp (vpp) yet. For simplicity, we remove the list
        # reward_model = nn.ModuleList(reward_model)

        if self.config.load_weight:
            if self.config.megatron.use_dist_checkpointing:
                load_mcore_dist_weights(reward_model, self.config.megatron.dist_checkpointing_path, is_value_model=True)
            else:
                load_megatron_gptmodel_weights(
                    self.config, self.hf_config, reward_model, params_dtype=self.dtype, is_value_model=True
                )

        # TODO: add more optimizer args into config
        torch.cuda.empty_cache()
        return reward_model, self.hf_config

    @register(dispatch_mode=Dispatch.ONE_TO_ALL)
    def init_model(self):
        # create critic
        from omegaconf import OmegaConf

        from verl.utils.torch_dtypes import PrecisionType

        if self.config.model.get("external_lib", None) is not None:
            # This is used to import external_lib into the huggingface systems
            import importlib

            importlib.import_module(self.config.model.external_lib)
        override_model_config = OmegaConf.to_container(self.config.model.get("override_config", OmegaConf.create()))

        sft_tokenizer_local_path = copy_to_local(self.config.model.input_tokenizer)
        sft_tokenizer = hf_tokenizer(sft_tokenizer_local_path)
        rm_tokenizer_path = self.config.model.get("rm_tokenizer", None)
        rm_tokenizer = None
        if rm_tokenizer_path is not None:
            rm_tokenizer_local_path = copy_to_local(rm_tokenizer_path)
            rm_tokenizer = hf_tokenizer(rm_tokenizer_local_path)

        self.param_dtype = torch.bfloat16
        self.dtype = PrecisionType.to_dtype(self.param_dtype)

        reward_model_module, reward_model_config = self._build_rm_model(
            model_path=self.config.model.path,
            override_model_config=override_model_config,
        )
        # FIXME(sgm): reward model param offload is implemented in MegatronRewardModel
        # should be implemented in workers
        self.rm = MegatronRewardModel(
            config=self.config,
            reward_model_module=reward_model_module,
            model_config=reward_model_config,
            hf_config=self.hf_config,
            tf_config=self.tf_config,
            sft_tokenizer=sft_tokenizer,
            rm_tokenizer=rm_tokenizer,
        )

    # TODO: reward model use itself tokenizer instead of sft tokenizer
    # the input_ids, responses, attention_mask and position_ids may be different!
    @register(dispatch_mode=Dispatch.MEGATRON_COMPUTE_PROTO)
    def compute_rm_score(self, data: DataProto):
        data.batch = data.batch.cuda()
        output = self.rm.compute_reward(data)
        output = output.to("cpu")
        return output
